UK Map Plots#

Inputs#

import pandas as pd
from pathlib import Path

import src.utils.map_utils as mu
import os
import sys

file =  os.path.join(cwd , "data", "combined_df.csv")

df = pd.read_csv(file, index_col=0)
address_col = "address_area"
df_comb = df.groupby(address_col,as_index=False).mean(numeric_only = True)
df_comb
address_area amount_detected mrl amount_pc
0 Bedfordshire 0.387000 2.373583 0.124811
1 Berkshire 0.196747 4.103357 0.742296
2 Bristol 0.268374 3.802430 0.170047
3 Buckinghamshire 0.050278 2.033333 0.098907
4 Cambridgeshire 0.512300 3.449771 0.092681
5 Central Scotland 0.064576 2.270339 0.220675
6 Cheshire 0.331614 3.441020 0.185064
7 City of London 0.488811 3.619672 0.045385
8 Cornwall 1.455588 7.805294 0.227373
9 County Durham 0.294703 4.774372 0.665944
10 Cumbria 0.206752 2.578974 0.073641
11 Derbyshire 0.296042 2.601042 0.086572
12 Devon 0.142708 3.093401 0.705448
13 Dorset 0.191635 1.853846 0.069592
14 East Riding of Yorkshire 0.294501 2.818758 0.241039
15 East Sussex 0.143893 2.445503 0.130867
16 Essex 0.468930 3.579789 0.324051
17 Glasgow 0.040593 1.450247 0.620967
18 Gloucestershire 0.115163 2.559455 0.109277
19 Greater London 0.224690 2.950398 0.168093
20 Greater Manchester 0.296786 3.689802 0.218737
21 Hampshire 0.538559 4.580920 1.157321
22 Herefordshire 1.370909 6.681515 0.099744
23 Hertfordshire 0.170821 3.124670 0.323446
24 Kent 0.344766 3.836676 0.097400
25 Lancashire 0.660758 5.865546 0.630129
26 Leicestershire 0.421726 4.812516 0.425529
27 Lincolnshire 0.720843 4.731526 0.090207
28 Lothian 0.134729 3.337831 0.079409
29 Merseyside 0.231206 3.589798 0.300602
30 Mid Scotland and Fife 0.165261 2.422155 0.355315
31 Mid Wales 0.060000 2.200000 0.026667
32 Norfolk 0.664853 4.288339 0.099100
33 North East Scotland 0.155392 2.930369 0.580549
34 North Wales 0.116242 2.562393 0.440996
35 North Yorkshire 0.275897 4.012571 0.124751
36 Northamptonshire 0.189718 2.327817 0.151279
37 Northern Ireland 0.203477 3.522473 0.320456
38 Northumberland 3.262500 5.000000 0.326250
39 Nottinghamshire 0.143738 4.401028 0.328896
40 Oxfordshire 0.136477 2.394375 0.074221
41 Shropshire 0.408955 3.994234 0.087272
42 Somerset 0.229284 3.201288 1.566792
43 South East Wales 1.259286 6.635000 0.131259
44 South Scotland 0.037000 0.332500 0.220143
45 South Wales 0.304305 3.650880 0.887286
46 South West Wales 0.247053 3.380015 0.100246
47 South Yorkshire 0.276270 4.235202 0.068786
48 Staffordshire 0.349470 4.100943 0.076136
49 Suffolk 0.335813 3.599019 0.153761
50 Surrey 0.139565 3.829293 0.494966
51 Tyne and Wear 0.303136 3.644100 0.440443
52 Warwickshire 0.432370 4.770462 0.149471
53 West Midlands 0.169469 2.723310 0.263257
54 West Scotland 0.139068 2.318882 0.164127
55 West Sussex 0.034615 1.788462 0.037361
56 West Yorkshire 0.301004 4.128642 0.858317
57 Wiltshire 0.122906 2.839687 0.075442
58 Worcestershire 0.174147 2.542257 0.163471
m = mu.plot_map(df, 
             what_to_plot='amount_pc',region_to_plot=address_col,
             json_path=os.path.join(cwd,'src//utils//map_data//combined_json.json'),
             longitude=-3.1, latitude=54.1)
m
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